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• W4360752541 startingPage "10100" @default.
• W4360752541 abstract "While uniform sampling has been widely studied in the matrix completion literature, CUR sampling approximates a low-rank matrix via row and column samples. Unfortunately, both sampling models lack flexibility for various circumstances in real-world applications. In this work, we propose a novel and easy-to-implement sampling strategy, coined Cross-Concentrated Sampling (CCS). By bridging uniform sampling and CUR sampling, CCS provides extra flexibility that can potentially save sampling costs in applications. In addition, we also provide a sufficient condition for CCS-based matrix completion. Moreover, we propose a highly efficient non-convex algorithm, termed Iterative CUR Completion (ICURC), for the proposed CCS model. Numerical experiments verify the empirical advantages of CCS and ICURC against uniform sampling and its baseline algorithms, on both synthetic and real-world datasets." @default.
• W4360752541 created "2023-03-24" @default.
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• W4360752541 date "2023-08-01" @default.
• W4360752541 modified "2023-10-16" @default.
• W4360752541 title "Matrix Completion With Cross-Concentrated Sampling: Bridging Uniform Sampling and CUR Sampling" @default.
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• W4360752541 doi "https://doi.org/10.1109/tpami.2023.3261185" @default.
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• W4360752541 hasPublicationYear "2023" @default.
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